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基于光伏阵列重构的电网有功调度响应方法

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随着电力系统新能源场站比例增加,系统特性发生了明显改变,因此新能源场站参与电网频率调节对系统稳定性具有重要现实意义.提出了一种光伏阵列重构策略,以解决光伏场站输出功率最大化和调频信号响应之间的平衡问题.首先建立了光伏场站阵列重构模型;然后以光伏场站输出功率与有功功率调度指令之间的功率偏差最小为目标函数,基于 AEO算法获得有效的帕累托前沿;最后设置算例,使用恒定调频信号和时变调频信号对所提算法有效性进行分析,并与SA算法进行比较.算例结果显示,所提算法与 SA 算法的偏差在恒定调频信号下分别为 9%和10%,在时变调频信号下分别为 0.7%和 2.2%,表明了所提算法在响应有功功率调度和支持电网有功功率方面的有效性.
Response Method for Active Power Dispatching of Power Grid Based on Photovoltaic Array Reconfiguration
As the proportion of new energy generations in the power system increases,the system characteristics have un-dergone significant changes.The participation of new energy generations in grid frequency regulation has important practi-cal significance for system stability.A photovoltaic array reconstruction strategy is proposed to solve the balance between maximizing the output power of photovoltaic stations and responding to frequency modulation signals.First a photovoltaic station array reconstruction model is established.Then taking the minimum power deviation between the output power of photovoltaic stations and the active power dispatching command as the objective function,an effective Pareto frontier is obtained based on the artificial ecosystem-based optimization(AEO)algorithm.Finally,a numerical example is set up,and constant frequency modulation signals and time-varying frequency modulation signals are used for analysis,and com-pared with the simulated annealing(SA)algorithm.The results of the numerical example show that,under constant fre-quency modulation signals,the deviations of the proposed method and the SA algorithm are 9%and 10%,respectively,and under time-varying frequency modulation signals,the deviations are 0.7%and 2.2%,respectively,indicating the ef-fectiveness of the proposed method in responding to active power dispatching and supporting grid active power.

photovoltaic plantarray reconfigurationactive power dispatchingfrequency regulation

霍炬霖

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长园深瑞继保自动化有限公司,广东 深圳 518057

光伏场站 阵列重构 有功调度 调频

2024

电工技术
重庆西南信息有限公司(原科技部西南信息中心)

电工技术

影响因子:0.177
ISSN:1002-1388
年,卷(期):2024.(21)